Seleção da Database

Database:

Brazilian E-Commerce Public Database by Olist https://www.kaggle.com/olistbr/brazilian-ecommerce?select=olist_order_items_dataset.csv

Descrição:

A database foi provida pela Olist, uma empresa de marketplace situada no Brasil. A Olist conecta pequenos negócios por todo o Brasil de uma forma simples. Vendedores conseguem vender seus produtos diretamente pela Olist Store, as entregas são feitas por meio de parceiros.

Problema de Negócio:

Quais são os principais fatores que alavancam o número de vendas? Quais os fatores que impactam na avaliação do cliente?

Observação:

Esse é o segundo notebook, onde iremos realizar clusterização e analisar os mesmos afim de promover insights. No primeiro notebook com o prefixo '01', realizamos toda a visualização do conjunto, onde foram retirados alguns insights que serão utilizados ao longo das proximas etapas.

import pandas as pd
import numpy as np
import re
import plotly.graph_objects as go

from warnings import simplefilter
from functools import reduce
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from math import ceil
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from dash import dash_table
from IPython.core.display import HTML, display

# Versões dos pacotes usados neste jupyter notebook
%reload_ext watermark
%watermark -a "Herikc Brecher e João Hutner" --iversions

Author: Herikc Brecher e João Hutner

dash  : 2.0.0
re    : 2.2.1
plotly: 5.1.0
pandas: 1.2.4
numpy : 1.19.5

simplefilter(action='ignore', category=FutureWarning)

# Seed de Aleatoriedade
seed_ = 194
np.random.seed(seed_)

1. Carregamento dos Dados

# Carregamento de todos datasets
dtCustomers = pd.read_csv('../data/olist_customers_dataset.csv', encoding = 'utf8', dtype={'customer_zip_code_prefix': str})
dtOrderItems = pd.read_csv('../data/olist_order_items_dataset.csv', encoding = 'utf8')
dtOrderPayments = pd.read_csv('../data/olist_order_payments_dataset.csv', encoding = 'utf8')
dtOrderReviews = pd.read_csv('../data/olist_order_reviews_dataset.csv', encoding = 'utf8')
dtOrders = pd.read_csv('../data/olist_orders_dataset.csv', encoding = 'utf8')

2. Pre-Processamento

2.1 Feature Engineering

Iremos adicionar uma variavel extra para todos os datasets que contém o Estado. A variavel adicionada é a Região referente ao Estado.

regioes = {
    'AC': 'Norte',
    'AL': 'Nordeste',
    'AP': 'Norte',
    'AM': 'Norte',
    'BA': 'Nordeste',
    'CE': 'Nordeste',
    'DF': 'CentroOeste',
    'ES': 'Sudeste',
    'GO': 'CentroOeste',
    'MA': 'Nordeste',
    'MT': 'CentroOeste',
    'MS': 'CentroOeste',
    'MG': 'Sudeste',
    'PA': 'Norte',
    'PB': 'Nordeste',
    'PR': 'Sul',
    'PE': 'Nordeste',
    'PI': 'Nordeste',
    'RJ': 'Sudeste',
    'RN': 'Nordeste',
    'RS': 'Sul',
    'RO': 'Norte',
    'RR': 'Norte',
    'SC': 'Sul',
    'SP': 'Sudeste',
    'SE': 'Nordeste',
    'TO': 'Norte'
}

dtCustomers['regiao'] =  dtCustomers['customer_state'].copy()

dtCustomers = dtCustomers.replace({'regiao': regioes})

dtCustomers.head()

	customer_id	customer_unique_id	customer_zip_code_prefix	customer_city	customer_state	regiao
0	06b8999e2fba1a1fbc88172c00ba8bc7	861eff4711a542e4b93843c6dd7febb0	14409	franca	SP	Sudeste
1	18955e83d337fd6b2def6b18a428ac77	290c77bc529b7ac935b93aa66c333dc3	09790	sao bernardo do campo	SP	Sudeste
2	4e7b3e00288586ebd08712fdd0374a03	060e732b5b29e8181a18229c7b0b2b5e	01151	sao paulo	SP	Sudeste
3	b2b6027bc5c5109e529d4dc6358b12c3	259dac757896d24d7702b9acbbff3f3c	08775	mogi das cruzes	SP	Sudeste
4	4f2d8ab171c80ec8364f7c12e35b23ad	345ecd01c38d18a9036ed96c73b8d066	13056	campinas	SP	Sudeste

2.2 Construindo Datasets

Devido ao problema em questão ser dividido em 8 datasets diferentes, iremos construir datasets mais unificados afim de facilitar o processo de tratamento dos dados e clusterização.

dtCustomers.head()

	customer_id	customer_unique_id	customer_zip_code_prefix	customer_city	customer_state	regiao
0	06b8999e2fba1a1fbc88172c00ba8bc7	861eff4711a542e4b93843c6dd7febb0	14409	franca	SP	Sudeste
1	18955e83d337fd6b2def6b18a428ac77	290c77bc529b7ac935b93aa66c333dc3	09790	sao bernardo do campo	SP	Sudeste
2	4e7b3e00288586ebd08712fdd0374a03	060e732b5b29e8181a18229c7b0b2b5e	01151	sao paulo	SP	Sudeste
3	b2b6027bc5c5109e529d4dc6358b12c3	259dac757896d24d7702b9acbbff3f3c	08775	mogi das cruzes	SP	Sudeste
4	4f2d8ab171c80ec8364f7c12e35b23ad	345ecd01c38d18a9036ed96c73b8d066	13056	campinas	SP	Sudeste

dtOrderItems.head()

	order_id	order_item_id	product_id	seller_id	shipping_limit_date	price	freight_value
0	00010242fe8c5a6d1ba2dd792cb16214	1	4244733e06e7ecb4970a6e2683c13e61	48436dade18ac8b2bce089ec2a041202	2017-09-19 09:45:35	58.90	13.29
1	00018f77f2f0320c557190d7a144bdd3	1	e5f2d52b802189ee658865ca93d83a8f	dd7ddc04e1b6c2c614352b383efe2d36	2017-05-03 11:05:13	239.90	19.93
2	000229ec398224ef6ca0657da4fc703e	1	c777355d18b72b67abbeef9df44fd0fd	5b51032eddd242adc84c38acab88f23d	2018-01-18 14:48:30	199.00	17.87
3	00024acbcdf0a6daa1e931b038114c75	1	7634da152a4610f1595efa32f14722fc	9d7a1d34a5052409006425275ba1c2b4	2018-08-15 10:10:18	12.99	12.79
4	00042b26cf59d7ce69dfabb4e55b4fd9	1	ac6c3623068f30de03045865e4e10089	df560393f3a51e74553ab94004ba5c87	2017-02-13 13:57:51	199.90	18.14

dtOrderPayments.head()

	order_id	payment_sequential	payment_type	payment_installments	payment_value
0	b81ef226f3fe1789b1e8b2acac839d17	1	credit_card	8	99.33
1	a9810da82917af2d9aefd1278f1dcfa0	1	credit_card	1	24.39
2	25e8ea4e93396b6fa0d3dd708e76c1bd	1	credit_card	1	65.71
3	ba78997921bbcdc1373bb41e913ab953	1	credit_card	8	107.78
4	42fdf880ba16b47b59251dd489d4441a	1	credit_card	2	128.45

dtOrderReviews.head()

	review_id	order_id	review_score	review_comment_title	review_comment_message	review_creation_date	review_answer_timestamp
0	7bc2406110b926393aa56f80a40eba40	73fc7af87114b39712e6da79b0a377eb	4	NaN	NaN	2018-01-18 00:00:00	2018-01-18 21:46:59
1	80e641a11e56f04c1ad469d5645fdfde	a548910a1c6147796b98fdf73dbeba33	5	NaN	NaN	2018-03-10 00:00:00	2018-03-11 03:05:13
2	228ce5500dc1d8e020d8d1322874b6f0	f9e4b658b201a9f2ecdecbb34bed034b	5	NaN	NaN	2018-02-17 00:00:00	2018-02-18 14:36:24
3	e64fb393e7b32834bb789ff8bb30750e	658677c97b385a9be170737859d3511b	5	NaN	Recebi bem antes do prazo estipulado.	2017-04-21 00:00:00	2017-04-21 22:02:06
4	f7c4243c7fe1938f181bec41a392bdeb	8e6bfb81e283fa7e4f11123a3fb894f1	5	NaN	Parabéns lojas lannister adorei comprar pela I...	2018-03-01 00:00:00	2018-03-02 10:26:53

dtOrders.head()

	order_id	customer_id	order_status	order_purchase_timestamp	order_approved_at	order_delivered_carrier_date	order_delivered_customer_date	order_estimated_delivery_date
0	e481f51cbdc54678b7cc49136f2d6af7	9ef432eb6251297304e76186b10a928d	delivered	2017-10-02 10:56:33	2017-10-02 11:07:15	2017-10-04 19:55:00	2017-10-10 21:25:13	2017-10-18 00:00:00
1	53cdb2fc8bc7dce0b6741e2150273451	b0830fb4747a6c6d20dea0b8c802d7ef	delivered	2018-07-24 20:41:37	2018-07-26 03:24:27	2018-07-26 14:31:00	2018-08-07 15:27:45	2018-08-13 00:00:00
2	47770eb9100c2d0c44946d9cf07ec65d	41ce2a54c0b03bf3443c3d931a367089	delivered	2018-08-08 08:38:49	2018-08-08 08:55:23	2018-08-08 13:50:00	2018-08-17 18:06:29	2018-09-04 00:00:00
3	949d5b44dbf5de918fe9c16f97b45f8a	f88197465ea7920adcdbec7375364d82	delivered	2017-11-18 19:28:06	2017-11-18 19:45:59	2017-11-22 13:39:59	2017-12-02 00:28:42	2017-12-15 00:00:00
4	ad21c59c0840e6cb83a9ceb5573f8159	8ab97904e6daea8866dbdbc4fb7aad2c	delivered	2018-02-13 21:18:39	2018-02-13 22:20:29	2018-02-14 19:46:34	2018-02-16 18:17:02	2018-02-26 00:00:00

2.2.1 Unificando Dataset de Pedidos

listDtMergeOrders = [dtOrderItems[['order_id', 'freight_value']], dtOrderPayments, dtOrderReviews, dtOrders,\
                    dtOrderItems.groupby('order_id').agg({'order_item_id': max}).reset_index()]

dtGeneralOrders = reduce(lambda left, right: pd.merge(left, right, on = 'order_id'), listDtMergeOrders)

dtGeneralOrders = pd.merge(dtGeneralOrders, dtCustomers[['customer_id', 'customer_city', 'customer_state', 'regiao']],\
                           on = 'customer_id')

dtGeneralOrders.head()

	order_id	freight_value	payment_sequential	payment_type	payment_installments	payment_value	review_id	review_score	review_comment_title	review_comment_message	...	order_status	order_purchase_timestamp	order_approved_at	order_delivered_carrier_date	order_delivered_customer_date	order_estimated_delivery_date	order_item_id	customer_city	customer_state	regiao
0	00010242fe8c5a6d1ba2dd792cb16214	13.29	1	credit_card	2	72.19	97ca439bc427b48bc1cd7177abe71365	5	NaN	Perfeito, produto entregue antes do combinado.	...	delivered	2017-09-13 08:59:02	2017-09-13 09:45:35	2017-09-19 18:34:16	2017-09-20 23:43:48	2017-09-29 00:00:00	1	campos dos goytacazes	RJ	Sudeste
1	00018f77f2f0320c557190d7a144bdd3	19.93	1	credit_card	3	259.83	7b07bacd811c4117b742569b04ce3580	4	NaN	NaN	...	delivered	2017-04-26 10:53:06	2017-04-26 11:05:13	2017-05-04 14:35:00	2017-05-12 16:04:24	2017-05-15 00:00:00	1	santa fe do sul	SP	Sudeste
2	000229ec398224ef6ca0657da4fc703e	17.87	1	credit_card	5	216.87	0c5b33dea94867d1ac402749e5438e8b	5	NaN	Chegou antes do prazo previsto e o produto sur...	...	delivered	2018-01-14 14:33:31	2018-01-14 14:48:30	2018-01-16 12:36:48	2018-01-22 13:19:16	2018-02-05 00:00:00	1	para de minas	MG	Sudeste
3	00024acbcdf0a6daa1e931b038114c75	12.79	1	credit_card	2	25.78	f4028d019cb58564807486a6aaf33817	4	NaN	NaN	...	delivered	2018-08-08 10:00:35	2018-08-08 10:10:18	2018-08-10 13:28:00	2018-08-14 13:32:39	2018-08-20 00:00:00	1	atibaia	SP	Sudeste
4	00042b26cf59d7ce69dfabb4e55b4fd9	18.14	1	credit_card	3	218.04	940144190dcba6351888cafa43f3a3a5	5	NaN	Gostei pois veio no prazo determinado .	...	delivered	2017-02-04 13:57:51	2017-02-04 14:10:13	2017-02-16 09:46:09	2017-03-01 16:42:31	2017-03-17 00:00:00	1	varzea paulista	SP	Sudeste

5 rows × 23 columns

# Mantendo somente Ano, Mes e Dia
dtGeneralOrders['order_purchase_timestamp'] = \
    pd.to_datetime(dtGeneralOrders['order_purchase_timestamp'], format = '%Y-%m-%d %H:%M:%S').dt.to_period('d')

# Mantendo somente Ano, Mes e Dia
dtGeneralOrders['order_delivered_customer_date'] = \
    pd.to_datetime(dtGeneralOrders['order_delivered_customer_date'], format = '%Y-%m-%d %H:%M:%S').dt.to_period('d')

# Mantendo somente Ano, Mes e Dia
dtGeneralOrders['order_estimated_delivery_date'] = \
    pd.to_datetime(dtGeneralOrders['order_estimated_delivery_date'], format = '%Y-%m-%d %H:%M:%S').dt.to_period('d')

dtGeneralOrders['diff_delivery_and_estimate'] = dtGeneralOrders['order_estimated_delivery_date'] -\
                                           dtGeneralOrders['order_delivered_customer_date']

dtGeneralOrders['diff_delivery_and_purchase'] = dtGeneralOrders['order_purchase_timestamp'] -\
                                           dtGeneralOrders['order_delivered_customer_date']

dtGeneralOrders['diff_delivery_and_estimate'] = \
                dtGeneralOrders['diff_delivery_and_estimate'].apply(lambda x: re.sub("[^0-9]", "", str(x)))

dtGeneralOrders['diff_delivery_and_purchase'] = \
                dtGeneralOrders['diff_delivery_and_purchase'].apply(lambda x: re.sub("[^0-9]", "", str(x)))

dtGeneralOrders['diff_delivery_and_estimate'] = pd.to_numeric(dtGeneralOrders['diff_delivery_and_estimate'])

dtGeneralOrders['diff_delivery_and_purchase'] = pd.to_numeric(dtGeneralOrders['diff_delivery_and_purchase'])

dtGeneralOrders.head()

	order_id	freight_value	payment_sequential	payment_type	payment_installments	payment_value	review_id	review_score	review_comment_title	review_comment_message	...	order_approved_at	order_delivered_carrier_date	order_delivered_customer_date	order_estimated_delivery_date	order_item_id	customer_city	customer_state	regiao	diff_delivery_and_estimate	diff_delivery_and_purchase
0	00010242fe8c5a6d1ba2dd792cb16214	13.29	1	credit_card	2	72.19	97ca439bc427b48bc1cd7177abe71365	5	NaN	Perfeito, produto entregue antes do combinado.	...	2017-09-13 09:45:35	2017-09-19 18:34:16	2017-09-20	2017-09-29	1	campos dos goytacazes	RJ	Sudeste	9.0	7.0
1	00018f77f2f0320c557190d7a144bdd3	19.93	1	credit_card	3	259.83	7b07bacd811c4117b742569b04ce3580	4	NaN	NaN	...	2017-04-26 11:05:13	2017-05-04 14:35:00	2017-05-12	2017-05-15	1	santa fe do sul	SP	Sudeste	3.0	16.0
2	000229ec398224ef6ca0657da4fc703e	17.87	1	credit_card	5	216.87	0c5b33dea94867d1ac402749e5438e8b	5	NaN	Chegou antes do prazo previsto e o produto sur...	...	2018-01-14 14:48:30	2018-01-16 12:36:48	2018-01-22	2018-02-05	1	para de minas	MG	Sudeste	14.0	8.0
3	00024acbcdf0a6daa1e931b038114c75	12.79	1	credit_card	2	25.78	f4028d019cb58564807486a6aaf33817	4	NaN	NaN	...	2018-08-08 10:10:18	2018-08-10 13:28:00	2018-08-14	2018-08-20	1	atibaia	SP	Sudeste	6.0	6.0
4	00042b26cf59d7ce69dfabb4e55b4fd9	18.14	1	credit_card	3	218.04	940144190dcba6351888cafa43f3a3a5	5	NaN	Gostei pois veio no prazo determinado .	...	2017-02-04 14:10:13	2017-02-16 09:46:09	2017-03-01	2017-03-17	1	varzea paulista	SP	Sudeste	16.0	25.0

5 rows × 25 columns

dtGeneralOrders = dtGeneralOrders[['order_id', 'freight_value', 'payment_type', 'payment_installments', 'payment_value',\
                                 'review_score', 'order_status', 'order_item_id',\
                                  'diff_delivery_and_estimate', 'diff_delivery_and_purchase',\
                                  'customer_state', 'regiao']]

dtGeneralOrders = dtGeneralOrders[dtGeneralOrders['order_status'] == 'delivered']

dtGeneralOrders = dtGeneralOrders.drop('order_status', axis = 1)

dtGeneralOrders = dtGeneralOrders.rename(columns = {'order_item_id': 'quantity', 'regiao': 'customer_region'})

# Dropando valores NA
dtGeneralOrders = dtGeneralOrders.dropna()

dtGeneralOrders.head()

	order_id	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region
0	00010242fe8c5a6d1ba2dd792cb16214	13.29	credit_card	2	72.19	5	1	9.0	7.0	RJ	Sudeste
1	00018f77f2f0320c557190d7a144bdd3	19.93	credit_card	3	259.83	4	1	3.0	16.0	SP	Sudeste
2	000229ec398224ef6ca0657da4fc703e	17.87	credit_card	5	216.87	5	1	14.0	8.0	MG	Sudeste
3	00024acbcdf0a6daa1e931b038114c75	12.79	credit_card	2	25.78	4	1	6.0	6.0	SP	Sudeste
4	00042b26cf59d7ce69dfabb4e55b4fd9	18.14	credit_card	3	218.04	5	1	16.0	25.0	SP	Sudeste

2.3 Preparando os Dados para Clusterização

colunas_quantitativas = ['freight_value', 'payment_value', 'quantity',\
                         'diff_delivery_and_estimate', 'diff_delivery_and_purchase']

2.3.1 LabelEncoder

dtGeneralOrders.head()

	order_id	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region
0	00010242fe8c5a6d1ba2dd792cb16214	13.29	credit_card	2	72.19	5	1	9.0	7.0	RJ	Sudeste
1	00018f77f2f0320c557190d7a144bdd3	19.93	credit_card	3	259.83	4	1	3.0	16.0	SP	Sudeste
2	000229ec398224ef6ca0657da4fc703e	17.87	credit_card	5	216.87	5	1	14.0	8.0	MG	Sudeste
3	00024acbcdf0a6daa1e931b038114c75	12.79	credit_card	2	25.78	4	1	6.0	6.0	SP	Sudeste
4	00042b26cf59d7ce69dfabb4e55b4fd9	18.14	credit_card	3	218.04	5	1	16.0	25.0	SP	Sudeste

dtGeneralOrdersProcessado = dtGeneralOrders.copy()

# Realizando LabelEncoder
labelencoderPaymentType = LabelEncoder()
labelencoderCustomerState = LabelEncoder()
labelencoderCustomerRegion = LabelEncoder()

dtGeneralOrdersProcessado['payment_type'] = labelencoderPaymentType.fit_transform(dtGeneralOrders['payment_type'])

dtGeneralOrdersProcessado['customer_state'] =\
                labelencoderCustomerState.fit_transform(dtGeneralOrders['customer_state'])

dtGeneralOrdersProcessado['customer_region'] =\
                labelencoderCustomerRegion.fit_transform(dtGeneralOrders['customer_region'])

dtGeneralOrdersProcessado.head()

	order_id	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region
0	00010242fe8c5a6d1ba2dd792cb16214	13.29	1	2	72.19	5	1	9.0	7.0	18	3
1	00018f77f2f0320c557190d7a144bdd3	19.93	1	3	259.83	4	1	3.0	16.0	25	3
2	000229ec398224ef6ca0657da4fc703e	17.87	1	5	216.87	5	1	14.0	8.0	10	3
3	00024acbcdf0a6daa1e931b038114c75	12.79	1	2	25.78	4	1	6.0	6.0	25	3
4	00042b26cf59d7ce69dfabb4e55b4fd9	18.14	1	3	218.04	5	1	16.0	25.0	25	3

2.3.2 Normalização / Padronização

scaler = StandardScaler()
#scaler = MinMaxScaler()

dtGeneralOrdersNormalizado = dtGeneralOrdersProcessado.copy()

dtGeneralOrdersNormalizado[colunas_quantitativas] = scaler.fit_transform(dtGeneralOrdersProcessado[colunas_quantitativas])

dtGeneralOrdersFinal = dtGeneralOrdersNormalizado.drop('order_id', axis = 1)

dtGeneralOrdersFinal.head()

	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region
0	-0.426745	1	2	-0.375092	5	-0.354355	-0.564103	-0.567311	18	3
1	-0.005297	1	3	0.329257	4	-0.354355	-1.298337	0.385421	25	3
2	-0.136047	1	5	0.167997	5	-0.354355	0.047760	-0.461452	10	3
3	-0.458481	1	2	-0.549303	4	-0.354355	-0.931220	-0.673170	25	3
4	-0.118910	1	3	0.172389	5	-0.354355	0.292504	1.338153	25	3

2.4.4 Analise de Clusters

# Criando um modelo
modelo = KMeans(n_clusters = 8, random_state = seed_)
modelo.fit(dtGeneralOrdersFinal)

KMeans(random_state=194)

dtGeneralOrdersClusterizado = dtGeneralOrdersProcessado.copy()

dtGeneralOrdersClusterizado = dtGeneralOrdersClusterizado.drop('order_id', axis = 1)

dtGeneralOrdersClusterizado['cluster'] = modelo.labels_

dtGeneralOrdersClusterizado['payment_type'] =\
                    labelencoderPaymentType.inverse_transform(dtGeneralOrdersClusterizado['payment_type'])

dtGeneralOrdersClusterizado['customer_state'] =\
                    labelencoderCustomerState.inverse_transform(dtGeneralOrdersClusterizado['customer_state'])

dtGeneralOrdersClusterizado['customer_region'] =\
                    labelencoderCustomerRegion.inverse_transform(dtGeneralOrdersClusterizado['customer_region'])

metodos_pagamento = {
    'credit_card': 'Credito',
    'debit_card': 'Debito'
}

dtGeneralOrdersClusterizado = dtGeneralOrdersClusterizado.replace({'payment_type': metodos_pagamento})

dtGeneralOrdersClusterizado.head()

	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region	cluster
0	13.29	Credito	2	72.19	5	1	9.0	7.0	RJ	Sudeste	2
1	19.93	Credito	3	259.83	4	1	3.0	16.0	SP	Sudeste	1
2	17.87	Credito	5	216.87	5	1	14.0	8.0	MG	Sudeste	0
3	12.79	Credito	2	25.78	4	1	6.0	6.0	SP	Sudeste	1
4	18.14	Credito	3	218.04	5	1	16.0	25.0	SP	Sudeste	1

dtClusters = pd.DataFrame()

dtClusters['Preco_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['payment_value'].mean(), 2)

dtClusters['Frete_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['freight_value'].mean(), 2)

dtClusters['Media_Pagamentos'] =\
                round(dtGeneralOrdersClusterizado.groupby('cluster')['payment_installments'].mean(), 2)

dtClusters['Score_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['review_score'].mean(), 2)

dtClusters['Itens_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['quantity'].mean(), 2)

dtClusters['Dif_Estimado_Entregue'] =\
                round(dtGeneralOrdersClusterizado.groupby('cluster')['diff_delivery_and_estimate'].mean(), 2)

dtClusters['Dif_Compra_Entregue'] =\
                round(dtGeneralOrdersClusterizado.groupby('cluster')['diff_delivery_and_purchase'].mean(), 2)

dtClusters['Pagamento_Prevalente'] =\
                dtGeneralOrdersClusterizado.groupby('cluster')['payment_type'].agg(lambda x: x.value_counts().index[0])

dtClusters['Estado_Prevalente'] =\
                dtGeneralOrdersClusterizado.groupby('cluster')['customer_state'].agg(lambda x: x.value_counts().index[0])

dtClusters['Regiao_Prevalente'] =\
                dtGeneralOrdersClusterizado.groupby('cluster')['customer_region'].agg(lambda x: x.value_counts().index[0])

display(HTML("<style>.container { width:100% !important; }</style>"))

dtClusters.to_csv('analise_clusters.csv')

dtClustersIndexado = dtClusters.reset_index()

dtClustersIndexado = dtClustersIndexado.rename(columns = {'cluster': 'Cluster'})

dtClustersIndexado['Cluster'] = dtClustersIndexado['Cluster'].apply(lambda x: 'Cluster ' + str(x))

headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'

fig = go.Figure(data=[go.Table(
  header=dict(
    values=list(dtClustersIndexado.columns),
    line_color='darkslategray',
    fill_color=headerColor,
    align=['left','center'],
    font=dict(color='white', size=12)
  ),
  cells=dict(
    values=[dtClustersIndexado.Cluster, dtClustersIndexado.Preco_Medio, dtClustersIndexado.Frete_Medio, dtClustersIndexado.Media_Pagamentos,\
       dtClustersIndexado.Score_Medio, dtClustersIndexado.Itens_Medio, dtClustersIndexado.Dif_Estimado_Entregue,\
       dtClustersIndexado.Dif_Compra_Entregue, dtClustersIndexado.Pagamento_Prevalente, dtClustersIndexado.Estado_Prevalente,\
       dtClustersIndexado.Regiao_Prevalente],
    line_color='darkslategray',
    
    fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor]*10],
    align = ['left', 'center'],
    font = dict(color = 'darkslategray', size = 11)
    ))
])

fig.show()